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Article:

Pinto-Sanchez, MI, Hall, GB, Ghajar, K et al. (19 more authors) (2017) Probiotic Bifidobacterium longum NCC3001 Reduces Depression Scores and Alters Brain Activity: aPilot Study in Patients With Irritable Bowel Syndrome. Gastroenterology, 153 (2). 448-459.e8. ISSN 0016-5085

https://doi.org/10.1053/j.gastro.2017.05.003

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Accepted Manuscript

Probiotic Bifidobacterium longum NCC3001 Reduces Depression Scores and AltersBrain Activity: a Pilot Study in Patients With Irritable Bowel Syndrome

Maria Ines Pinto-Sanchez, MD, Geoffrey B. Hall, PhD, Kathy Ghajar, BSc,Andrea Nardelli, MD, Carolina Bolino, MD, Jennifer T. Lau, BSc, Francois-PierreMartin, PhD, Ornella Cominetti, PhD, Christopher Welsh, BSc, Amber Rieder,BA, Jenna Traynor, BSc, Caitlin Gregory, MD, Giada De Palma, PhD, MarcPigrau, MD, Alexander C. Ford, MD, Joseph Macri, PhD, Bernard Berner, PhD,Gabriela Bergonzelli, PhD, Michael G. Surette, PhD, Stephen M. Collins, MD, PaulMoayyedi, MD, Premysl Bercik, MD.

PII: S0016-5085(17)35557-9DOI: 10.1053/j.gastro.2017.05.003Reference: YGAST 61167

To appear in: GastroenterologyAccepted Date: 2 May 2017

Please cite this article as: Pinto-Sanchez MI, Hall GB, Ghajar K, Nardelli A, Bolino C, Lau JT, MartinF-P, Cominetti O, Welsh C, Rieder A, Traynor J, Gregory C, De Palma G, Pigrau M, Ford AC, MacriJ, Berner B, Bergonzelli G, Surette MG, Collins SM, Moayyedi P, Bercik P, Probiotic Bifidobacteriumlongum NCC3001 Reduces Depression Scores and Alters Brain Activity: a Pilot Study in Patients WithIrritable Bowel Syndrome, Gastroenterology (2017), doi: 10.1053/j.gastro.2017.05.003.

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http://dx.doi.org/10.1053/j.gastro.2017.05.003

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Probiotic Bifidobacterium longum NCC3001 Reduces Depression Scores and Alters Brain

Activity: a Pilot Study in Patients With Irritable Bowel Syndrome.

Maria Ines Pinto-Sanchez1 MD, Geoffrey B Hall2 PhD, Kathy Ghajar2 BSc, Andrea Nardelli1 MD,

Carolina Bolino1 MD, Jennifer T Lau1 BSc, Francois-Pierre Martin3 PhD, Ornella Cominetti3 PhD,

Christopher Welsh1 BSc, Amber Rieder2 BA, Jenna Traynor2 BSc, Caitlin Gregory2 MD,

Giada De Palma1 PhD, Marc Pigrau1 MD, Alexander C Ford4 MD, Joseph Macri5 PhD,

Bernard Berner6 PhD, Gabriela Bergonzelli6 PhD, Michael G. Surette1 PhD,

Stephen M Collins1 MD, Paul Moayyedi1 MD, Premysl Bercik1 MD.

1Department of Medicine, Farncombe Family Digestive Health Research Institute, McMaster

University, Hamilton, ON, Canada

2Department of Psychology, Neuroscience & Behavior, McMaster University, Hamilton, ON,

Canada.

3Nestlé Institute of Health Sciences SA, Lausanne, Switzerland

4Leeds Gastroenterology Institute, St. James's University Hospital, & Leeds Institute of Biomedical

and Clinical Sciences, University of Leeds, Leeds, UK.

5Department of Pathology, McMaster University, Hamilton, ON, Canada.

6Nestlé Research Center, Nutrition Institute, Lausanne, Switzerland.

Short Title: B. longum decreases depression in IBS patients

Funding source: Nestlé SA, Switzerland,

Corresponding author: Premysl Bercik, MD

Department of Medicine, Farncombe Family Digestive Health Research Institute,

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McMaster University, Hamilton, ON, Canada

e-mail: [email protected]

CONFLICT OF INTEREST: MIPS, AN, GH, ACF, KG, AR, JT, CW, GDP, MP, JM, CG, CB,

JL, MS and PM have no conflicts of interest concerning this paper. GB, and BB are employees of

Nestec SA, Switzerland. MFP and OC are employees of Nestlé Institute of Health Sciences SA. PB

and SMC received research support from Nestlé.

DISCLOSURE: MIPS received a CIHR-CAG Health Professional Fellowship Award. PB is

recipient of HHS Early Career Research Award and holder of Richard Hunt-AstraZeneca Chair in

Gastroenterology.

MANUSCRIPT CONTRIBUTION

MIPS: acquisition, analysis and interpretation of data; statistical analysis, writing of the manuscript;

GH: study concept and design; acquisition, analysis and interpretation of fMRI data; KG AR, JT,

CG: fMRI data acquisition, analysis and interpretation; AN: clinical data acquisition, CB: study

concept and design; CW: database development and data acquisition; JL, MS: microbiota analysis,

critical review of manuscript; FPM, OC: NMR metabolomic analysis and data interpretation, BB:

microbiota analysis, critical revision of the manuscript for important intellectual content, GB: study

design, critical revision of the manuscript for important intellectual content: AF, SMC: critical

review of the manuscript and important intellectual content; PM: Study design, statistical analysis,

critical revision of the manuscript for important intellectual content; PB: Study design, critical

revision of the manuscript and supervision of the study. All authors reviewed and approved the last

version of the manuscript.

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Acknowledgments:

The authors thank Peter McLean and Mirna Del Valle for their invaluable support and study

monitoring and oversight. We also thank Tiago Nunes for thoughtful comments on the manuscript,

Mireille Goillard for neurotransmitter analysis, Francis Foata for the detection of BL and Margaret

Fahnestock for advice on BDNF analysis and Laeticia Da Silva and Dr. Sebastiano Collino for

their contribution in metabonomics data generation.

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ABSTRACT

Background & Aims: Probiotics can reduce symptoms of irritable bowel syndrome (IBS), but little

is known about their effects on psychiatric comorbidities. We performed a prospective study to

evaluate the effects of Bifidobacterium longum NCC3001 (BL) on anxiety and depression in

patients with IBS.

Methods: We performed a randomized, double-blind, placebo-controlled study of 44 adults with

IBS and diarrhea or a mixed-stool pattern (based on Rome III criteria) and mild to moderate anxiety

and/or depression (based on the Hospital Anxiety and Depression scale) at McMaster University in

Canada, from March 2011 to May 2014. At the screening visit, clinical history and symptoms were

assessed and blood samples were collected. Patients were then randomly assigned to groups and

given daily BL (n=22) or placebo (n=22) for 6 weeks. At week 0, 6 and 10, we determined patients’

levels of anxiety and depression, IBS symptoms, quality of life, and somatization using validated

questionnaires. At week 0 and 6, stool, urine and blood samples were collected, and functional

magnetic resonance imaging (fMRI) test was performed. We assessed brain activation patterns,

fecal microbiota, urine metabolome profiles, serum markers of inflammation, neurotransmitters and

neurotrophin levels.

Results: At week 6, 14/22 patients in the BL group had reduction in depression scores of 2 points or

more on the Hospital Anxiety and Depression scale, vs 7/22 patients in the placebo group (P=.04).

BL had no significant effect on anxiety or IBS symptoms. Patients in the BL group had a mean

increase in quality of life score compared with the placebo group. The fMRI analysis showed that

BL reduced responses to negative emotional stimuli in multiple brain areas, including amygdala and

fronto–limbic regions, compared with placebo. The groups had similar fecal microbiota profiles,

serum markers of inflammation, and levels of neurotrophins and neurotransmitters, but the BL

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group had reduced urine levels of methylamines and aromatic amino acids metabolites. At week 10,

depression scores were reduced in patients given BL vs placebo.

Conclusion: In a placebo-controlled trial, we found that the probiotic BL reduces depression but not

anxiety scores and increases quality of life in patients with IBS. These improvements were

associated with changes in brain activation patterns that indicate that this probiotic reduces limbic

reactivity. ClinicalTrials.gov no. NCT01276626.

Key words: IBS, anxiety, depression, fMRI

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BACKGROUND

Irritable bowel syndrome (IBS), characterized by abdominal pain and altered bowel habits, affects

11% of the world-wide population1, has a significant socioeconomic impact2 and its current

treatments have limited efficacy1. Its pathophysiology is incompletely understood but is considered

to be a disorder of gut-brain interaction3 and is frequently accompanied by psychiatric disorders1,4.

Accumulating evidence suggests that commensal bacteria play a role in IBS, as multiple studies

have demonstrated an abnormal composition or metabolic activity of gut microbiota in patients with

IBS5. Dysbiosis, triggered by acute bacterial gastroenteritis, antibiotics or dietary factors, which are

known to affect the composition of microbiota, may drive not only the gastrointestinal component

of IBS but also contribute to its psychiatric comorbidity6. Furthermore, specific probiotic bacteria

have been shown to improve gastrointestinal symptoms in IBS7.

We have previously demonstrated that administration of B. longum NCC3001 subspecies longum

strain (BL) normalized anxiety-like behavior and hippocampal Brain Derived Neurotrophic Factor

(BDNF) levels in mice with low-grade gut inflammation, through vagal dependent pathways8,9.

Based on these results, we hypothesized that BL will improve psychiatric comorbidity in patients

with chronic bowel disorders and thus we performed a pilot study in IBS patients. As anxiety and

depression are rather difficult to distinguish in animal models, they frequently co-exist in patients

and altered central BDNF levels were reported in both conditions, we chose as our primary

objective to evaluate the impact of BL on co-morbid anxiety and depression. The secondary

objectives were then to assess the effect of BL on IBS symptoms and quality of life, and to explore

changes in brain activation patterns, circulating inflammatory markers, neurotransmitters,

neurotrophins, gut microbiota profile and urine metabolites as a measure of host-microbial

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metabolic interactions. Considering the large heterogeneity of IBS we decided to restrict our study

to IBS patients with diarrhea or mixed stool pattern, as they apper to share similar sensory neuro-

imune interaction and are more likely to present with low-grade gut inflammation and similar

microbiota compared with IBS patients with constipation10,5.

Although several clinical studies investigated effects of probiotic bacteria on behavior and brain

function11-12, mostly in healthy individuals, our study is the first one to show that probiotics can

improve depression scores as well as alter brain activity patterns in IBS patients with comorbid

depression and anxiety.

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METHODS

Study oversight

We conducted a randomized, double-blind, placebo-controlled, single center pilot study from

March 2011 to May 2014. The study was approved by the Hamilton Health Sciences and St.

Joseph’s Health Care Research Ethics Boards, all participants signed the informed consent. The

study was registered in clinicaltrials.gov under NCT01276626. All authors had access to the study

data and reviewed and approved the final manuscript.

Participants

We recruited adult patients with a diagnosis of IBS with diarrhea or mixed-stool pattern (Rome III

criteria)13, and mild to moderate anxiety and/or depression scores based on the Hospital Anxiety

and Depression (HAD) scale14 (HAD-A or HAD-D score 8-14). Patients with a history of organic

diseases, immune deficiency, major abdominal surgery, a psychiatric condition other than anxiety

or depression, use of immunosuppressants, glucocorticosteroids, opioids, antidepressants or

anxiolytics in regular doses, alcohol or illicit drug consumption, were excluded. Loperamide and

laxatives were allowed as rescue medications. Other probiotics in any form were forbidden during

the 1-month run-in period and the trial. Antibiotics were forbidden during the 3 months prior to the

run-in period and the trial.

Design of the study

The study involved four hospital visits (Supplementary figure 1). At the screening visit ( -4 weeks),

clinical history and symptoms were assessed and physical exam and complete bloodwork

performed. At the baseline visit (week 0), the inclusion and exclusion criteria and symptoms were

re-assessed, stool, urine and blood samples were collected, and an fMRI study performed.

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The patients were then randomised to receive 42 sachets of either spray dried BL (1.0E+10 CFU

/1gram powder with maltodextrin) or placebo containing 1 gram of maltodextrin. Treatment

products were indistinguishable in terms of package, color, taste and consistency. Patients were

instructed to dissolve the content of the sachet in 100-200 ml of lactose-free milk, soy milk or rice

milk, preheated to 20° Celsius. Patients were asked not to change their eating habits or fibre intake.

Participants recorded the treatment intake, the empty sachets were used to assess the compliance at

the next visit (week 6), where their symptoms were assessed, blood, urine and stool samples

collected and fMRI test performed. Finally, patients’ symptoms were re-assessed at a follow-up

visit (week 10).

In addition to the regular hospital visits, HAD scores were also assessed at 3 weeks of treatment

following request of Health Canada. HAD questionnaires were provided to patients at Visit 1 and

then mailed or e-mailed to the investigators.

Study endpoints

The primary endpoint was a reduction in anxiety and/or depression scores of ≥2 points on HAD

scale13 at 6 weeks. This was based on the previously established mean clinically important

difference for the anxiety and depression score on the HAD scale of 1.3 and 1.4, respectively14.

Secondary endpoints included improvement in anxiety and depression scores (HAD, continuous

data), anxiety (State-Trait Anxiety Inventory, STAI), IBS global adequate relief, IBS symptoms,

somatization, quality of life, changes in brain activation patterns (functional Magnetic Resonance

Imaging, fMRI), serum inflammatory markers, neurotransmitters and BDNF, and urine

metabonomic and stool microbiota profiles.

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Randomization

The randomization sequence was performed using a computer program (Proc Plan, SAS, V. 9.1). A

block randomization was stratified by gender and IBS status (diarrhea or mixed stool pattern). The

codes were kept in sealed opaque envelopes allocated to patients according to strata. Each pack was

assigned a number according to the randomization sequence. On recruitment, the patients were

assigned into one of four strata and given the next consecutive randomization number available for

that stratum. Treatment allocation was concealed from participants and study staff.

Treatment products indistinguishable in terms of package, color, taste and consistency, were

identified with two non-speaking codes per arm. Their identity was blind to subjects, investigators

and support staff, known only by the manufacturer, Nestlé Product Technology Centre Konolfingen

Switzerland.

Study Measurements

Anxiety and depression were assessed by the HAD score. As an additional measure of anxiety we

used the STAI15, which assesses both state and trait anxiety. IBS symptoms and signs were assessed

by the Birmingham IBS score16 and Bristol stool scale17. To evaluate an overall improvement of

IBS symptoms, patients were asked a validated question: “Over the past week have you had

adequate relief of your IBS symptoms?” with a dichotomous option for responses18. Health-related

quality of life (QoL) was measured by the SF-3619 and somatization by the PHQ-15

questionnaires20.

Brain activity was assessed by functional magnetic resonance imaging (fMRI) using General

Electric 3-Tesla Discovery MR 750, whole body short bore scanner with 32 parallel receiver

channels (General Electric, Milwaukee, WI). The 1-hour protocol included a seven minute T1

weighted structural scan, followed by four repetitions of a fearful face backward masking

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paradigm21 (Supplementary figure 2) during four fMRI Blood Oxygen Level Dependent scans22

(BOLD EPI; TR/TE=2800/35 ms, flip angle=90º, 3 mm thick slices, no gap, field of view=24 cm,

matrix=64x64). Pre-processing of MRI data was completed using Brain Voyager QX Version 2.8.2,

32-bit (Brain Innovation, Maastricht, Netherlands). Anatomic and functional data were inspected

and scans with artefacts or fMRI scans with movement greater than 5 mm in any of 6 planes were

excluded from analysis. Anatomical scans were transformed into standard sagittal orientation, and

underwent spatial normalization into standard Talaraich space. Slice scan time correction and 3D

motion correction were carried out on the fMRI data and spatial smoothing applied using a

Gaussian filter (FWHM=6 mm). The amygdala was selected as region of interest (ROI), initially

derived from the WFUPick Atlas and refined according to anatomic landmarks on the full group

average transformed T1 image.

Blood and urine samples were collected after an overnight fast. After processing, the samples were

stored at -80 C until assessment. Samples for BDNF were collected using the PAXgene Blood RNA

(PreAnalytiX, Qiagen BD, Toronto, Canada). Serum cytokines and CRP levels were assessed by

Human ProInflammatory 7-Plex Ultra-Sensitive Kit (MSD, Gaithersburg, MD) and CRP Abbott

Architect kit (Abbott Laboratories, IL), respectively. BDNF protein level was assessed by Human

BDNF DuoSet ELISA kit (R&D Systems, Minneapolis, MN). Plasma neurotransmitters were

quantified using following kits: 5-HT: IBL, Hamburg, Germany; Substance P: Abcam, Cambridge,

UK; CGRP: Cloud-Clone Corp, Houston, TX.

Urine metabolites were assessed by 1H NMR profiling using a Bruker Avance II 600 MHz

spectrometer equipped with a 1.7 mm probe at 300 K (Bruker Biospin, Rheinstetten, Germany),

using a standard pulse sequence with water suppression, and processed using TOPSPIN (version

2.1, Bruker, Germany) software package. The metabolite identification was achieved using in house

database and 2D 1H NMR spectroscopy experiments. Chemometric analysis was performed using

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the software package SIMCA-P+ (version 14.0, Umetrics AB, Umeå, Sweden) and in-house

developed MATLAB routines. Orthogonal Projection to Latent Structures (OPLS) and OPLS

discriminant analysis (OPLS-DA) were employed for exploring the variance in the metabonomics

data that may explain statistical differences between groups of samples. The classification accuracy

of the OPLS-DA was established from the predicted samples in the 7-fold cross-validation cycle.

To highlight the weight of individual variables in the model, Variable Importance in Projection

(VIP) was used, with a value above 1 used as a threshold by convention. For additional details, see

Supplementary Methods.

Microbiota analysis was performed using Illumina sequencing of the V3 region of 16S rRNA gene

as described previously23, for details see Supplementary Methods. Bacterial strain-specific PCR24

was used on fecal DNA extracts to detect the presence of BL at the end of the treatment period.

Statistical analysis

Statistical analyses were performed using IBM-SPSS (IBM-SPSS Statistics v20, Chicago, IL). We

performed post-hoc power calculations based on our previous animal data, which showed strong

therapeutic potential of this probiotic8-9. We estimated that a sample size of 19 in each group would

have 80% power using a two-group χ2 test with 0.05 two-sided significance level assuming 30%

have an improvement in depression and/or anxiety in the placebo group and 75% in the B. longum

group.

Data from all randomized subjects were analyzed according to intention to treat (ITT) principles for

the primary outcome. To deal with missing data, we used the extreme case analysis assuming that

all missing subjects had no improvement in symptoms. Per protocol evaluation (PP) excluded data

from subjects who did not complete the trial due to consumption of proscribed medication or non-

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compliance with the study protocol, and was used for the primary and secondary outcomes. For

testing the effects on the two primary endpoints, Pearson Chi-Squared and Mann-Whitney U test

were used as appropriate. In addition, the HAD scores were analyzed at baseline, week 3, 6 and 10

post-treatment using ANOVA repeated measures. ANCOVA was used to adjust for baselines

differences in HAD depression scores. A two-sided test was used and p

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Nestec SA was not involved in collection, analysis or interpretation of the clinical data. The

corresponding author was in charge of collection and analysis of data and had final responsibility

for the decision to submit for publication.

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RESULTS

Study patients

Sixty patients were enrolled in the study (Figure 1). Sixteen patients failed the screening due to: 1)

very mild IBS symptoms (n=2) or low HAD score (n=1); 2) use of antibiotics (n=2); 3)

unwillingness to follow the protocol (n=7), or 5) loss to follow-up (n=4). Thus, 44 patients were

randomized (22 in each study arm), from whom 38 completed the study (BL=18, placebo=20).

During the treatment, six patients dropped out due to use of antibiotics (n=4), or antidepressants

(n=2). No differences were observed between the groups in baseline characteristics (Table 1),

except for higher HAD-D scores in the BL group (Table 2).

Primary outcome: Improvement in depression and/or anxiety scores

At 6 weeks, 14 out of 22 (64%) patients in the BL group had decreased depression scores (HAD-D

≥2 points) compared with 7 out of 22 (32%) patients given placebo (relative risk (RR) 1.98; 95%

confidence interval (CI) 1.16-3.38; p=0.04) in the ITT population (Table 3A). This response was

more prominent in the PP analysis with 78% in the BL group compared with 35% of the placebo

group having lower depression scores (RR 2.4; 95% CI 1.26-4.58; p=0.016). The improvement in

HAD-D scores was sustained in both the ITT (RR 2.05; 95% CI 1.07-3.93; p=0.04) and the PP

analysis (RR 2.14; 95% CI 1.11-4.12; p=0.04) at 10-week follow-up. The results at 6 weeks were

similar when the analysis was performed in only the subgroup of patients with baseline scores

indicative of depression (HAD-D ≥8), (RR 3.75; 95% CI 0.6-22.1; p=0.047). No significant

differences in number of patients with decreased anxiety (HAD-A ≥2 points) were found between

the groups at 6 or 10-week follow-up.

A sensitivity analysis performed to explore the relationship between depression scores and

gastrointestinal symptoms showed that the beneficial effect of BL on depression scores at 6 and 10

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weeks was more likely to occur in those patients who reported adequate relief of IBS symptoms

(Table 3B).

Secondary outcomes

HAD scores

After treatment, there were no significant differences on HAD-D scores when assessed as a

continuous outcome between the two groups (Table 2), but when adjusting for baseline differences

a greater improvement was found in the BL group (ANCOVA p=0.049). Anxiety scores assessed as

a continuous outcome were similar between the two groups (Table 2).

IBS symptoms

There was no difference in adequate relief of IBS symptoms in the ITT analysis at 6 weeks (RR 1.6,

95% CI 0.86-2.91; p=0.22) but the PP analysis showed a statistically significant benefit of BL over

placebo (RR 2.03 95% CI 1.13-3.65; p=0.02). This beneficial effect was not, however, maintained

at 10-week follow-up (RR 0.70, 95% CI 0.24-2.09; p=0.52). No significant differences were

observed in the overall Birmingham score or sub scores for constipation, diarrhea or pain at 6 or 10

weeks (Table 2).

State and trait anxiety and somatization

No differences in STAI scores were observed between groups at 6-week or 10-week follow-up

(Table 2). Somatization scores were also similar between groups at 6-week or 10-week follow up

(Table 2).

Quality of life

QoL improved in the physical subdomain in the BL group compared with placebo (p=0.03; 95% CI

0.01-0.90) (Table 2), with amelioration in general physical health (physical functioning; p=0.04;

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95% CI 0.43-0.51) and problems with work or other daily activities (role physical; p=0.01; 95% CI

0.009-0.013).

Brain activation patterns by fMRI

Brain activation patterns differed in response to fearful versus happy faces in all subjects studied

(Supplementary Figure 3, Supplementary Table 1). Before treatment, there was no difference in

response to fear stimuli versus fixation between placebo and the BL group, except for greater

engagement of the visual association and parietal cortices in the latter group (Figure 2A-B).

However, after treatment, compared with placebo, the BL group showed reduced engagement of the

amygdala, frontal and temporal cortices, as well as heightened engagement of occipital regions in

response to the fear stimuli (compared with fixation) (Figure 2B-C, Table 4). Overall, the change in

engagement of the amygdala correlated with the change in depression scores (r=0.52, p=0.004;

Figure 2E). Within the BL group, reduced engagement of the amygdala correlated with decreased

depression scores (r=0.58, p=0.03), but this was not observed in the placebo group (r=0.20,

p=0.46). In the BL group, reduced engagement of the amygdala was more likely to occur in patients

with adequate relief of IBS symptoms than in those without it (RR 3.07 95% CI 0.89-10.59; p=0.03),

but this was not observed in the control group (RR 1.5 95% CI 1.00-2.23; p=0.51; Figure 2E).

There was no difference when comparing response to fearful versus neutral faces.

Inflammatory markers, neurotransmitters and BDNF levels

No differences in serum inflammatory markers (CRP, TNF-α, IFN-γ, IL-1β, IL-6, IL-8, IL-10, IL12

and IL-10/12 ratio) or neurotransmitters (5-HT, substance P and CGRP) were found between the

groups at 6 weeks (Supplementary table 2). Similarly, no changes in blood BDNF mRNA or

BDNF protein levels were identified.

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Intestinal microbiota composition

There were no major differences in taxa, compositional distance or alpha diversity (Shannon, Chao1

and Observed Species indices) before or after the treatment between the two groups. Microbiota

profiles were also similar when assessed by Bray-Curtis Principle Coordinate analysis or Bray-

Curtis Distance comparisons (Supplementary Figure 4). BL was detected at the end of treatment in

15 out of 18 (80%) patients of the probiotic group.

Urine metabonomics

OPLS discriminant analysis was applied using one predictive and two orthogonal components to

model urine metabolic differences between the two groups (Supplementary Figure 5). The model

was statistically robust only for post-treatment analysis (R2X=0.17, R2Y=0.84, Q2Y=0.20, where

R2X: explained variance in the metabonomics data (urine metabolites), R2Y: explained group

variance (placebo and probiotic) and Q2Y: robustness of the model). Before treatment, there was

no difference between the two groups (Q2Y

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DISCUSSION

In this randomized, placebo controlled study we found that 6-week administration of

Bifidobacterium longum NCC3001 (BL) decreased depression but not anxiety scores, which was

our primary outcome, and decreased responses to fearful stimuli in multiple brain areas involved in

the processing of emotions, including the amygdala and fronto–limbic regions. Patients given BL

also reported improvement in overall symptoms of IBS and physical domain of QoL. Despite being

a pilot study with limited subject numbers, this is the first trial to show that a specific probiotic

improves depression scores in IBS patients and induces pronounced changes in brain activity in

regions that have been previously implicated in depression, and that are influenced by anti-

depressant therapy27,28. Indeed, both depression and anxiety disorders have been associated with

amygdala hyperactivity and converging evidence suggests that one mechanism by which Selective

Serotonin Re-uptake Inhibitors (SSRIs) exert their beneficial action is by downregulating its

activity27,28. Our study also validates the use of some murine models8,9 to screen probiotics for their

potential therapeutic benefit in humans, as BL was shown previously to improve anxiety-like

behavior and brain chemistry in mice.

There is growing interest in the role of the intestinal microbiota in health and disease. Gut bacteria

not only instruct and shape the host immune system, and impact its metabolism, but also affect

function in the gut and central nervous system29,30. Animal studies have demonstrated that changes

in microbiota composition31 or administration of specific probiotics, including bifidobacteria, can

alter behavior and brain chemistry of the host8,9,32. Multiple mechanisms are likely involved in this

microbiota-brain communication, including immune, neural and metabolic pathways30. We have

previously extensively investigated the beneficial effect of BL on behavior in murine models and

shown that this probiotic normalizes inflammation-induced anxiety-like behavior and hippocampal

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BDNF levels8,9. The beneficial effect on behavior was mediated through vagal signaling, possibly

through release of neuroactive metabolites acting on enteric neurons9.

Despite accumulating evidence of the influence of microbiota on behavior in animal models, data

from humans are rather limited30. Two studies in healthy volunteers reported no major effects of

probiotic on anxiety and depression scores33,34. A very recent pilot study in patients with major

depression showed that depression improved in both the placebo and probiotic groups, although the

improvement appeared to be greater in the latter35. The only study, which directly linked the gut

microbiota to brain function, assessed effects of mixture of probiotic bacteria in healthy females.

Although no change in behavior in that trial was observed, fMRI showed decreased BOLD activity

in the limbic and sensory brain regions in response to facial expressions, which stimulate emotional

responses12. We used a similar emotive challenge that is known to induce activation in several brain

regions, including the amygdala, where the fear response is particularly salient36, and which was

thus chosen as a predetermined ROI. We found that BL administration decreased activation of the

amygdala and the fronto–limbic complex compared with placebo, which was paralleled by

decreased depression scores. The amygdala is not only involved in regulation of fear and anxiety,

but also in activation of the hypothalamic-pituitary-adrenal axis (HPA)37 and modulation of the

visceral sensitivity38. In our study, pain scores appeared to improve more in the BL group compared

to the placebo group, although the difference was not statistically significant. However, the BL

group was more likely to report the adequate relief of IBS symptoms and this was associated with

decreased amygdala activation. Thus changes in pain perception could explain the overall

improvement of IBS symptom in the BL group but this should be further investigated in future,

better powered trials.

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Multiple studies have demonstrated beneficial effects of different probiotics in patients with IBS,

and a recent meta-analysis7 has shown that bifidobacteria are more likely to improve

gastrointestinal symptoms than lactobacilli. Underlying mechanisms may involve improvement of

low-grade gut inflammation present in a subset of patients with IBS39. Inflammation has also been

linked to psychiatric disorders as pro-inflammatory cytokines can affect mood by a number of

mechanisms, including activation of the HPA axis and alteration of the metabolism of

neurotransmitters40. We found no differences in serum CRP or cytokines levels between placebo

and patients given BL, indicating that the beneficial effect of this probiotic is not mediated by

improvement in inflammation, in agreement with the previous study in mice8.

Our murine study demonstrated that hippocampal BDNF was upregulated by BL8. Neurotrophin

BDNF influences survival and differentiation of neurons, formation of functional synapses and

neuroplasticity41 and is decreased in major depressive disorder42. We found no differences in serum

BDNF or neurotransmitter levels, including serotonin, in patients given BL compared with patients

in the placebo group but this does not rule out the possibility that BDNF or neutransmitters levels

in specific brain regions were modulated by the probiotic.

As changes in gut microbiota composition have been linked to altered behavior and brain

chemistry31, we assessed fecal bacterial profiles before and after the treatment. Analysis of fecal

16S rRNA gene sequencing suggests that the beneficial effect of BL is independent of major

alterations in microbioal composition. However, BL altered the urinary metabolic profile suggestive

of downregulated bacterial degradation of methylamines and aromatic amino acids43, including a

decrease in host-bacterial co-metabolite 4-cresol sulfate, a by-product of tyrosine fermentation44.

Interestingly, 4-cresol sulfate is known to inhibit dopamine β-hydroxylase45, which is a constituent

of the catecholamine biosynthetic pathway involved in mood regulation46. This enzyme converting

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dopamine into noradrenaline is expressed in the central and peripheral nervous system as well as in

chromaffin cells of the adrenal medulla47, and its decreased activity has been associated with

depression46. In our study, levels of 4-cresol sulfate correlated with improved depression scores in

BL group suggesting that dopamine/noradrenaline pathway may play an important role in the effect

of this probiotic. In future studies, a targeted metabolomic approach should provide further insight

into the impact of BL on the bacterial metabolism of aromatic amino acids and the subsequent

modulation of the host’s catecholamine production.

Although this is the first randomized trial to show that probiotics decrease depression scores in IBS

patients there are limitations that are important to emphasize. We used the HAD scale to identify

patients with psychiatric comorbidity, which is well validated and widely used in studies

investigating IBS as a simple tool to screen for comorbid anxiety or depression3. Its psychometric

properties may be inferior to clinician-administered rating scales for depression and anxiety, but its

main advantage is that it does not measure somatic symptoms13,48, a strong confounding factor in

any study of IBS patients. Indeed, there is no superior validated questionnaire that can be used in

patients with a concurrent medical disorder. However, a confirmatory trial using additional specific

psychometric tools in a larger cohort of patient is needed to substantiate our current results. A

weakness of our study was the difference in the baseline depression scores between the two groups

with lower values in the placebo group. The improvement in depression in the probiotic group

could relate to regression to the mean or reflect a floor effect. We believe this is unlikely, however,

as a statistically significant result in favor of BL remained when adjusting for baseline differences

using, and when performing analysis only in the subgroup of patients with baseline scores

indicative of depression (HAD-D ≥8). Also, there was a linear decrease in the depression subscale

of the HAD score in the BL group that did not asymptote with the placebo HAD score

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(Supplementary Figure 6). Furthermore, the observed changes in brain activation patterns in the

areas involved in mood regulation in the BL group support the notion that this probiotic has anti-

depressive properties. We have not shown any effect of BL on individual gut symptoms but our

study was not adequately powered to detect these changes. Thus, a larger, appropriately powered

trial with patients with IBS and comorbid depression is needed to verify our data on psychiatric and

gut symptoms before BL can be recommended in clinical practice.

In conclusion, B. longum NC3001 has longstanding beneficial effects on mild to moderate comorbid

depression and it temporarily improves overall symptoms of IBS and QoL. This is associated with

changes in activity of multiple brain areas involved in emotional processing, but no improvement in

individual gut symptoms, suggesting that central effects of this probiotic underlies its therapeutic

effect, possibly through modulation of host catecholamine production.

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44- Selmer T, Andrei PI. p-Hydroxyphenylacetate decarboxylase from Clostridium difficile. A

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45- Goodhart PJ, DeWolf WE Jr, Kruse LI. Mechanism-based inactivation of dopamine beta-

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46- Togsverd M, Werge TM, Tanko LB, et al. Association of a dopamine beta-hydroxylase gene

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47- Kapoor A, Shandilya M, Kundu S. Structural insight of dopamine beta-hydroxylase, a drug

target for complex traits, and functional significance of exonic single nucleotide polymorphisms.

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LEGENDS

Figure 1: Flowchart study population

Figure 2: Brain activation patterns assessed by fMRI

Functional Magnetic Resonance Imaging (fMRI) was used to assess the BOLD response to fearful

stimuli compared with fixation. Group differences in activation pattern are displayed. At baseline,

when examining all brain regions (A), there was slightly greater engagement of the visual

association (A1) and parietal cortices (A2) in B longum (BL) group compared with the placebo

group, with no difference in predetermined region of interest (ROI), amygdala (B). After treatment

(at 6 weeks), the BL group displayed lesser engagement of the amygdala, as well as frontal and

temporal cortices that are involved in anxiety and mood regulation (C, D; in yellow-orange) and

increased engagement of occipital regions (in blue) compared with the placebo group. At 6 week,

the amygdala activation correlated with the depression scores in the whole cohort and the BL group

(E, middle and right panel). Patients with adequate relief of IBS symptoms (blue dots) were more

likely to have lesser engagement of the amygdala than the patients without improvement in their

IBS symptoms (red dots).

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Table 1: Demographics characteristics of study population

B. longum n=22

Placebo n=22

Study status, n (%) Completed Dropped out

18 (82) 4 (18)

20 (91) 2 (9)

Age, median (IQR) 46.5 (30-58) 40.0 (26-57) Female, n (%) 12 (54) 12 (54) Ethnicity n (%) Caucasian Other

19 (86) 3 (14)

21 (95) 1 (5)

Smoking status Smokers, n ( %)

3 (14) 3 (14)

Alcohol consumption Consumers, n (%)

8 (36) 11 (50)

Fibre consumption, g/day, median (IQR)

18.0 (12.0-23.2) 13.5 (10.0-18.2)

BMI, median (IQR) 25.1 (21.5-28.4) 24.6 (22.3-29.5)

IBS subtype n (%) Female Diarrhoea Female Mixed Male Diarrhea Male Mixed

6 (27) 6 (27) 8 (37) 2 (9)

6 (27) 6 (27) 7 (32) 3 (14)

Anxiety and depression, n (%) Anxiety (HAD-A ≥8) Depression (HAD-D ≥8) Anxiety and depression

21 (95) 13 (59) 12 (54)

18 (82) 8 (36) 6 (27)

No significant differences between groups for demographics characteristics (P value for all

comparisons between groups >0.05)

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Table 2. Depression, anxiety, IBS symptoms, quality of life and somatization scores

Test/ Mean (SD) Before treatment Post-treatment, 6 weeks Mean difference

95% CI Follow-up, 10 weeks Mean difference

95% CI Placebo B. longum Placebo B. longum Placebo B. longum

Depression HAD-D 5.2

(3.0)

7.6

(3.7)

4.5

(3.1)

3.9

(3.1)

0.6

(-1.6 to 2.6)

4.7

(3.5)

4.7

(3.8)

0.15

(-2.5 to 2.5)

Anxiety HAD-A 9.3 (2.6) 10.2

(3.2)

7.1

(3.9)

6.5

(3.8)

0.6

(-1.9 to 3.2)

8.0

(4.3)

7.6

(4.8)

0.39

(-2.7 to 3.5)

Anxiety STAI 40.4

(12.3)

41.3

(13.9)

38.8

(12.9)

33.1

(9.5)

5.7

(-2.0 to 13.5)

37.6

(11.9)

38.4

(15.3)

-0.83

(-10.1 to 8.4)

Anxiety TAI 44.0

(11.9)

47.7

(10.5)

42.5

(11.3)

39.4

(11.6)

3.1

(-4.5 to 10.6)

42.2

(11.3)

32.3

(12.7)

2.84

(-5.2 to 10.9)

IBS-Birmingham:

total

17.8

(7.9)

17.7

(7.1)

12.6

(9.2)

8.8

(9.2)

3.8

(-2.4 to 9.9)

13.0

(6.9)

12.4

(9.5)

0.55

(-4.9 to 6.1)

Birmingham:

constipation

3.8

(3.6)

3.0

(3.1)

3.1

(3.5)

1.4

(1.6)

1.7

(-0.1 to 3.6)

3.2

(3.5)

2.5

(3.8)

0.71

(-1.7 to 3.2)

Birmingham:

diarrhea

8.2

(4.3)

8.5

(4.8)

4.7

(4.3)

4.1

(5.8)

0.5

(-2.8 to 3.9)

5.1

(3.2)

5.2

(4.9)

-0.17

(-2.9 to 2.6)

Birmingham:

Pain

6.3

(3.9)

6.2

(3.2)

4.9

(4.1)

3.4

(3.5)

1.5

(-1.1 to 4.1)

4.7

(3.4)

4.7

(3.8)

0.15

(-2.4 to 2.4)

QoL-SF-36: Physical 43.9

(10.8) 45.0

(10.1) 43.1 (9.9)

49.9 (8.8)

-6.8 * (-13.2 to -0.4)

46.9 (10.0)

46.4 (9.6)

0.52 (-6.3 to 7.4)

QoL-SF-36 Mental 41.9

(11.1)

39.4

(11.8)

43.3

(9.7)

47.1

(9.9)

-3.80

(-10.4 to 2.8)

41.3

(12.0)

46.4

(12.8)

-5.06

(-13.7 to 3.6)

QoL-SF-36:

Physical functioning

78.5

(22.5)

78.3

(24.2)

76.8

(22.7)

94.5

(9.5)

-14.7*

(-26.7 to -2.7)

79.2

(23.1)

83.3

(19.8)

-4.17

(-18.7 to 10.4)

QoL-SF-36:

Role physical

51.3

(38.4)

61.1

(43.1)

47.5

(38.8)

80.8

(35.9)

-33.4*

(-58.5 to -8.2)

59.7

(38.5)

62.5

(42.2)

-2.77

(-30.1 to 24.6)

QoL-SF-36:

Bodily pain

61.2

(24.2)

51.0

(20.1)

58.2

(23.3)

65.1

(20.7)

-6.9

(-21.7 to 7.9)

62.2

(25.5)

60.1

(20.4)

2.11

(-13.5 to 17.7)

QoL-SF-36:

General health

55.2

(19.2)

59.6

(20.9)

60.9

(22.1)

68.1

(17.0)

-7.3

(-20.6 to 6.1)

59.9

(20.7)

65.6

(21.1)

-5.61

(-19.8 to 8.5)

QoL-SF-36: Vitality 45.5

(19.2)

40.3

(18.7)

43.7

(18.9)

55.3

(17.4)

-11.5

(-23.8 to 0.7)

46.4

(20.4)

49.7

(25.0)

-3.33

(-18.8 to 12.1)

QoL-SF-36:

Social functioning

61.9

(21.6)

56.9

(26.8)

68.1

(24.4)

77.2

(19.4)

-9.08

(-24.0 to 5.8)

63.9

(26.7)

72.2

(22.5)

-8.33

(-25.1 to 8.4)

QoL-SF-36:

Role emotional

60.0

(39.9)

55.6

(39.6)

56.7

(40.6)

78.4

(38.9)

-21.8

(-48.5 to 4.9)

59.3

(43.6)

68.5

(43.5)

-9.25

(-38.7 to 20.2)

QoL-SF-36:

Mental health

62.0

(20.2)

59.6

(18.7)

65.6

(17.6)

71.5

(16.4)

-5.9

(-17.4 to 5.5)

64.0

(20.6)

69.3

(20.6)

-5.33

(-19.3 to 8.6)

PHQ15: Somatization 11.1 11.9 10.0 8.4 1.61 9.4 10.0 -0.63

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(2.9) (3.5) (3.9) (3.2) (-0.8 to 4.0) (3.5) (4.8) (-3.4 to 2.2)

*p

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Table 3: Decrease ≥2 points in depression and anxiety HAD scores at 6 and 10 weeks in the whole group (A) or when stratified by adequate relief of IBS symptoms (B)

Outcome B. longum

n Placebo

n ITT Analysis

RR 95% CI p value PP Analysis

RR 95% CI p value

Decrease HAD-D ≥2 at 6 weeks

14 7 1.98 1.16-3.38 0.04 2.40 1.26-4.58 0.01

Decrease HAD-A ≥2 at 6 weeks

14 11 1.31 0.72-2.42 0.54 1.69 0.76-3.77 0.19


13 6 2.05 1.07-3.93 0.04 2.14 1.11-4.12 0.04


12 10 1.4 0.65-2.82 0.50 1.6 0.77-3.17 0.34

Outcome

B. longum n

Placebo n

ITT Analysis RR 95% CI p value

PP Analysis RR 95% CI p value

Adequate relief of IBS symptoms


11 2 3.07 0.89-10.6 0.03 3.07 0.89-10.6 0.03


11 4 1.53 0.72-3.27 0.66 1.53 0.72-3.27 0.34


4 1 6.00 1.03-35.9 0.04 6.00 1.06-35.9 0.04


4 4 1.5 0.85-2.64 0.46 1.50 0.85-2.64 0.46

No Adequate relief of IBS symptoms


3 5 0.93 0.29-2.98 1.0 1.6 0.56-4.54 0.56


3 7 0.66 0.23-1.92 0.34 1.14 0.45-2.90 0.98


9 5 1.60 0.67-3.78 0.31 1.80 0.80-4.02 0.25

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8 6 1.18 0.52-2.68 0.73 1.33 0.62-2.84 0.70

Chi2 test, HAD-D: Hospital Anxiety and Depression score for depression, HAD-A: Hospital

Anxiety and Depression score for anxiety, ITT: Intention-to-treat analysis, PP: Per-protocol

analysis

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Table 4: Brain activation patterns in all brain regions (top) and the amygdala (bottom).

Left/right Brain region Brodmann

area T-value p-value

No.

voxels

Right Middle Temporal Gyrus BA 21 4.62796 0.000013 818


Right Cerebellum * 3.964027 0.000154 452

Right Inferior Frontal Gyrus BA 47 4.564617 0.000017 4657

Right Cuneus BA 19 -3.889998 0.0002 1268

Right Middle Occipital Gyrus BA 19 -4.455264 0.000026 898

Right Middle Frontal Gyrus BA 10 4.099587 0.000095 526

Right Amygdala/

Parahippocampal Gyrus BA 28 3.983939 0.000144 1474

Right Cuneus BA 19 -3.709217 0.000372 1527

Right Medial Frontal Gyrus BA 11 4.73075 0.000009 365

Left Middle Occipital Gyrus BA 19 -4.04685 0.000115 1196

Left Amygdala/

Parahippocampal Gyrus BA 35 4.451565 0.000026 1727

Left Middle Frontal Gyrus BA 10 3.687655 0.0004 1080


Left Middle Temporal Gyrus BA 21 4.097827 0.000096 689


Left/right Brain region T-value p-value No. voxels

Right Amygdala 3.98394 0.000144 690

Left Amygdala 4.031566 0.000121 212

Brain activation patterns assessed by fMRI for post-treatment group differences (placebo vs treatment; fear vs. fixation) in all brain regions (top) and for ROI- amygdala analyses (bottom). All data are corrected for multiple comparisons; FDR(q)

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Table 5: Urinary metabolites after 6 weeks of treatment assessed by 1H NMR

Urinary metabolites 1H NMR signal

(multiplicity) OPLS Correlation

coefficient (VIP)

Relative concentration (au) p-value

Placebo B. longum

Creatine 3.04 (s) -0.44 (1.36) 14.49 (10.3) 7.44 (3.0) 0.007

Phenylacetylglutamine 7.43 (m) -0.50 (1.76) 4.5 (1.8) 3.01 (1.3) 0.013

4-cresol sulfate 2.35 (s) -0.36 (1.42) 5.38 (2.3) 3.9 (1.7) 0.022

Trimethylamine-N-Oxide 3.27 (s) -0.32 (1.18) 44.0 (61.2) 15.48 (4.5) 0.002

Metabolite data are reported as mean (SD) with an arbitrary unit (au) derived from 1H NMR

spectral peak area. VIP: Variable Importance in Projection; m: multiplet; s: singlet.

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Figure 1.

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ROI - AmygdalaI

-2 -1 0 1 2-10

-5

0

5

-2 -1 0 1 2-10

-5

0

5

-2 -1 0 1 2-10

-5

0

5

D amygdala activation (% BOLD)

DH

AD

-D s

core

Pearson r=0.52, p=0.004

D amygdala activation (% BOLD)D amygdala activation (% BOLD)

DH

AD

-D s

core

DH

AD

-D s

core

Pooled Data BLPlacebo

Pearson r=0.53, p=0.04

Pearson r=0.14, p=0.02

Adequate relief of IBS symptoms No adequate relief of IBS symptoms

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Supplementary Figure 1: Design of the study

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Supplementary Figure 2: The backward masking paradigm.

The backward masking paradigm: The fixation was jittered, followed by the presentation of a

fearful or happy face for 67 ms, followed by the masking of the stimuli with a neutral face for 83

ms. Participants had 1800 ms to respond. Four presentations of the paradigm were collected, with

84 trials per presentation (scan).

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Supplementary Figure 3. Group responses to fear stimuli vs. happy or fixation stimuli.

A. Full group response to fear face stimuli at both time points contrasted with the full group

response to happy face stimuli. The analysis identified a number of brain regions that showed

greater engagement in response to the fear stimuli including the amygdala, insula and regions in the

frontal cortices.

B. Full group response to fear stimuli contrasted with full group response to fixation stimuli, at both

time points revealed broad activation including the fusiform gyri, thalamus, striatal regions, parietal

cortices and hippocampal/amygdala complex.

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Supplementary Figure 4: Gut microbiota analysis

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Supplementary Figure 5: Overview of 1H NMR urine metabolic profile and multivariate data

analysis

A: Typical 1H NMR urine metabolic profile with selected metabolite signature highlighted in

aliphatic (0.5 – 4.5 ppm) and aromatic spectral areas (6.5 – 9.0 ppm).

B: Overview of OPLS-DA coefficient plots resulting for urine NMR data and group discriminant

analysis (placebo top, probiotics bottom). The OPLS coefficients plots are presented using a back-

scaling transformation and projection to aid biomarker visualization. The direction of the signals in

the plots relative to zero indicates positive or negative covariance with the group of interest. Each

variable is plotted with a color code which indicates its discriminating power as calculated from the

correlation matrix thus highlighting biomarker rich spectral regions.

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Supplementary Figure 6: Differences in estimated marginal means for HAD-D and HAD-A

(ANOVA multiple measurements)

HAD-D: Hospital Anxiety and Depression score for depression, HAD-A: Hospital Anxiety and

Depression score for anxiety

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Supplementary table 1: Full group responses at both time points in response to fear vs. happy stimuli

Left/

right Brain region Brodmann area T P value No. voxels

Right Insula BA 13 5.4106 0.000008 3136

Right Superior Temporal Gyrus BA 22 4.8403 0.00004 7845

Right Inferior Temporal Gyrus BA 20 4.1807 0.000244 469

Right Inferior Parietal Lobule BA 40 3.9513 0.000456 1144




Right Superior Temporal Gyrus BA 38 -4.6615 0.000065 181

Right Cingulate Gyrus BA 24 4.4890 0.000105 1416

Right Insula BA 13 3.3460 0.002279 186

Right Lentiform Nucleus Lat. Globus

Pallidus 4.7033 0.000058 4401

Right Thalamus Pulvinar 4.6475 0.000067 1116

Right Paracentral Lobule BA 5 4.2963 0.000178 855

Left Parahippocampal Gyrus BA 27 4.3273 0.000163 306

Left Precuneus BA 19 3.6435 0.001043 589

Left Amygdala Amygdala 3.9502 0.000458 144

Left Caudate Caudate Tail 3.3883 0.002041 156

Left Insula BA 13 3.6715 0.000968 567

Left Lentiform Nucleus Putamen 4.2270 0.000215 978

Left Middle Occipital Gyrus BA 19 3.5165 0.00146 231

Left Inferior Frontal Gyrus BA 46 3.4428 0.001771 142

Left Middle Temporal Gyrus BA 21 3.78414 0.000717 505

Left Superior Temporal Gyrus BA 22 4.2784 0.000187 683

Left Inferior Temporal Gyrus BA 20 3.5100 0.001485 209

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Supplementary table 2: Inflammatory cytokines, BDNF and neurotransmitter levels at six weeks

Test / Median (IQR)

Baseline After treatment (6 weeks) U p

B. longum Placebo B. longum Placebo

CRP 1.12 (0.4-2.3) 0.85 (0.4-0.9) 1.32 (0.45-1.7) 1.70 (1.0-1.9) 120.5 0.18

TNF-α 0.98 (0.82-1.17) 0.96 (0.89-1.25) 0.97 (0.82-1.05) 1.06 (0.95-1.29) 116.0 0.33

IFN-γ 0.13 (0.07-0.22) 0.11 (0.08-0.15) 0.14 (0.10-0.18) 0.13 (0.09-0.18) 136.0 0.79

IL-6 0.22 (0.14-0.27) 0.20 (0.15-0.24) 0.18 (0.13-0.26) 0.23 (0.18-0.40) 90.0 0.06

IL-8 0.79 (0.64-1.42) 0.90 (0.65-1.17) 0.97 (0.70-1.14) 0.85 (0.66-1.11) 119.0 0.39

IL-1β 0.97 (0.70-1.14) 0.05 (0.02-0.07) 0.04 (0.02-0.05) 0.04 (0.01-0.06) 116.0 0.33

IL-10 1.32 (1.10-1.61) 1.39 (1.17-1.48) 1.45 (1.05-1.66) 1.69 (1.32-2.02) 101.0 0.14

IL-12 p70 0.09 (0.04-0.15) 0.05 (0.04-0.07) 0.06 (0.05-0.08) 0.06 (0.04-0.09) 136.0 0.78

IL10/12 ratio 26.5 (18.7-36.4) 24.1 (20.9-31.1) 19.7 (11.6-27.7) 24.4 (16.8-36.0) 114.0 0.30

BDNF / β actin 20 (20-28) 29 (17-52) 25 (17-41) 37 (21-55) 124.0 0.10

BDNF ELISA 6.61 (2.43-8.41) 4.87 (3.21-11.11) 7.00 (3.60-10.56) 5.83 (3.15-10.37) 148.0 0.65

CGRP 23.6 (17.1-28.8) 25.2 (18.8-30.8) 23.8 (17.4-30.3) 26.5 (20.9-32.7) 147.0 0.34

Substance P 1.01 (0.61-1.42) 1.26 (0.75-1.60) 1.03 (0.75-1.49) 1.19 (0.59-1.51) 176.0 0.91

Serotonin 12.3 (5.8-14.5) 10.2 (6.6-19.3) 8.3 (4.2-14.9) 8.9 (5.9-12.1) 165.0 0.67

Concentrations of individual biomarkers: CRP (mg/L), TNF-α, IFN-γ, IL-6, IL-8, IL-1β, IL-10 (all

pg/mL), BDNF/β actin Log10-5 (copies/ng RNA), BDNF ELISA (ng/mL) CGRP (pg/mL), Substance P

(ng/mL), Serotonin (ng/mL),

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Supplementary table 3: Most common adverse events

Adverse Event B. longum n

Placebo n

P value Causality Outcome

Constipation 0 2 0.48 NR NR

Recovered Recovered

Rectal bleeding 0 1 1.0 NR Recovered Rhinitis 2 1

1.0 NR NR NR

Recovered Recovered Recovered

Headaches 4 1

0.34

Possible Possible Possible Possible NR

Recovered Recovered Recovered Recovered Recovered

Oral vesicles 1 0 1.0 NR Recovered Anal fissure 1 0 1.0 NR Recovered Neck pain 0 1 1.0 NR Recovered Urine infection 1 0 1.0 NR Dropped* Nausea 0 1 1.0 NR Recovered GERD symptoms 2 1 1.0 NR

NR NR

Recovered Recovered Recovered

Abdominal pain 0 1 1.0 NR Recovered Diarrhoea 0 1 1.0 NR Recovered Cold 2 2 1.0 NR

NR NR NR

Recovered Recovered Recovered Recovered

Otitis 1 0 1.0 NR Dropped* Food allergy 1 0 1.0 NR Recovered Streptococcus pharyngitis

1 0 1.0 NR Dropped *

Back pain 1 0 1.0 NR Recovered Iritis 1 0 1.0 NR Recovered Anxiety attack 0 1 1.0 NR Recovered

Total 18 14 0.31

NR: Not related, *Dropped from the study due to use of antibiotics

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Supplementary methods:

Microbiota analysis:

Microbiota analysis was performed using Illumina sequencing of the V3 region of 16S rRNA gene

as described previously1. The data were processed by an in-house bioinformatics pipeline that

incorporates quality filtering, Cutadapt2, PandaSeq3, AbundantOTU4, mothur5 and QIIME6.

Abundant OTU provide output of clustered sequences in operational taxonomic units (OTUs).

Taxonomic assignments use the RDP classifier7 with the Greengenes training set8. Analysis

includes alpha-diversity measures for each sample and estimates of total diversity using QIIME, as

well as β-diversity measures (weighted and unweighted Unifrac, Bray-Curtis) and other statistical

analysis using QIIME and the PhyloSeq9 package implemented in R.

Metabonomics analysis:

1H NMR metabolite profiling approach was applied to urine samples, since this biofluid contains

useful time-averaged representations of the recent homeostatic metabolic history of the individual

and also carry indirect information on the gut microbial metabolic activities via the excretion

patterns of many polar microbial-mammalian co-metabolites10, 11 (Supplementary Figure 5A).

Forty µL of urine were mixed with 20 µL of deuterated phosphate buffer solution 0.6 M KH2PO4,

containing 1 mM of sodium 3-(trimethylsilyl)-[2,2,3,3-2H4]-1-propionate (TSP, chemical shift

reference δH = 0.0 ppm). 60µL of the mixture were transferred into 1.7mm NMR tubes. 1H NMR

spectra were acquired with a Bruker Avance II 600 MHz spectrometer equipped with a 1.7 mm

probe at 300 K (Bruker Biospin, Rheinstetten, Germany), using a standard pulse sequence with

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water suppression, and processed using TOPSPIN (version 2.1, Bruker, Germany) software

package. The metabolite identification was achieved using in house database and 2D 1H NMR

spectroscopy experiments.

Chemometric analysis was performed using the software package SIMCA-P+ (version 14.0,

Umetrics AB, Umeå, Sweden) and in-house developed MATLAB routines. Orthogonal Projection

to Latent Structures (OPLS)12 and OPLS discriminant analysis (OPLS-DA) were employed for

exploring the variance in the metabonomics data that may explain statistical differences between

groups of samples. The classification accuracy of the OPLS-DA was established from the predicted

samples in the 7-fold cross-validation cycle. To highlight the weight of individual variables in the

model, Variable Importance in Projection (VIP) was used, with a value above 1 used as a threshold

by convention. In addition, influential NMR variables that are correlated to the group separation are

identified using the variable coefficients according to a previously published methods13.

Representative signals of the identified metabolites were integrated and tested using non-parametric

Mann Whitney test.

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Additional references:

1- Bartram AK, Lynch MD, Stearns JC, et al. Generation of multimillion-sequence 16S rRNA gene

libraries from complex microbial communities by assembling paired-end illumina reads. Appl

Environ Microbiol 2011, 77(11): 3846-3852.

2- Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMB

netjournal 2011, 17(1): 10-12.

3- Masella AP, Bartram AK, Truszkowski JM, et al. PANDAseq: paired-end assembler for illumina

sequences. BMC Bioinformatics 2012, 13: 31.

4- Ye Y. Fast and accurate identification and quantification of abundant species from pyrosequences

of 16S rRNA by consensus alignment. The proceedings of BIBM 2010: 153-157.

5- Schloss PD, Westcott SL, Ryabin T, et al. Introducing mothur: open-source,platform-

independent, community-supported software for describing and comparing microbial

communities. Appl.Environ.Microbiol 2009, 75: 7537–7541.

6- Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput

community sequencing data. Nat Methods 2010, 7(5): 335-336.

7- Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of

rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 2007, 73:5261–5267.

8- DeSantis TZ, Hugenholtz P, Larsen N, et al. Greengenes, a chimerachecked 16S rRNA gene

database and workbench compatible with ARB. Appl Environ Microbiol 2006, 72:5069–5072.

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9- McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and

graphics of microbiome census data. PloS one 2013, 8(4): e61217.

10- Moco S, Collino S, Rezzi S, et al. Metabolomics perspectives in pediatric research. Pediatr Res

2013;73:570-6.

11- Collino S, Martin FP, Rezzi S. Clinical metabolomics paves the way towards future healthcare

strategies. Br J Clin Pharmacol 2013;75:619-29.

12- Trygg J, Wold S. O2-PLS, a two-block (X-Y) latent variable regression (LVR) method with an

integrated OSC filter. J Chemom 2003;17:53-64.

13- Cloarec O, Dumas ME, Craig A, et al. Statistical total correlation spectroscopy: an exploratory

approach for latent biomarker identification from metabolic 1 H NMR data sets. Anal Chem

2005;77:1282-1289.

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